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tesseract/lstm/lstmrecognizer.cpp

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///////////////////////////////////////////////////////////////////////
// File: lstmrecognizer.cpp
// Description: Top-level line recognizer class for LSTM-based networks.
// Author: Ray Smith
// Created: Thu May 02 10:59:06 PST 2013
//
// (C) Copyright 2013, Google Inc.
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
///////////////////////////////////////////////////////////////////////
// Include automatically generated configuration file if running autoconf.
#ifdef HAVE_CONFIG_H
#include "config_auto.h"
#endif
#include "lstmrecognizer.h"
#include "allheaders.h"
#include "callcpp.h"
#include "dict.h"
#include "genericheap.h"
#include "helpers.h"
#include "imagedata.h"
#include "input.h"
#include "lstm.h"
#include "normalis.h"
#include "pageres.h"
#include "ratngs.h"
#include "recodebeam.h"
#include "scrollview.h"
#include "shapetable.h"
#include "statistc.h"
#include "tprintf.h"
namespace tesseract {
// Max number of blob choices to return in any given position.
const int kMaxChoices = 4;
// Default ratio between dict and non-dict words.
const double kDictRatio = 2.25;
// Default certainty offset to give the dictionary a chance.
const double kCertOffset = -0.085;
LSTMRecognizer::LSTMRecognizer()
: network_(NULL),
training_flags_(0),
training_iteration_(0),
sample_iteration_(0),
null_char_(UNICHAR_BROKEN),
learning_rate_(0.0f),
momentum_(0.0f),
adam_beta_(0.0f),
dict_(NULL),
search_(NULL),
debug_win_(NULL) {}
LSTMRecognizer::~LSTMRecognizer() {
delete network_;
delete dict_;
delete search_;
}
// Loads a model from mgr, including the dictionary only if lang is not null.
bool LSTMRecognizer::Load(const char* lang, TessdataManager* mgr) {
TFile fp;
if (!mgr->GetComponent(TESSDATA_LSTM, &fp)) return false;
if (!DeSerialize(mgr, &fp)) return false;
if (lang == nullptr) return true;
// Allow it to run without a dictionary.
LoadDictionary(lang, mgr);
return true;
}
// Writes to the given file. Returns false in case of error.
bool LSTMRecognizer::Serialize(const TessdataManager* mgr, TFile* fp) const {
bool include_charsets = mgr == nullptr ||
!mgr->IsComponentAvailable(TESSDATA_LSTM_RECODER) ||
!mgr->IsComponentAvailable(TESSDATA_LSTM_UNICHARSET);
if (!network_->Serialize(fp)) return false;
if (include_charsets && !GetUnicharset().save_to_file(fp)) return false;
if (!network_str_.Serialize(fp)) return false;
if (fp->FWrite(&training_flags_, sizeof(training_flags_), 1) != 1)
return false;
if (fp->FWrite(&training_iteration_, sizeof(training_iteration_), 1) != 1)
return false;
if (fp->FWrite(&sample_iteration_, sizeof(sample_iteration_), 1) != 1)
return false;
if (fp->FWrite(&null_char_, sizeof(null_char_), 1) != 1) return false;
if (fp->FWrite(&adam_beta_, sizeof(adam_beta_), 1) != 1) return false;
if (fp->FWrite(&learning_rate_, sizeof(learning_rate_), 1) != 1) return false;
if (fp->FWrite(&momentum_, sizeof(momentum_), 1) != 1) return false;
if (include_charsets && IsRecoding() && !recoder_.Serialize(fp)) return false;
return true;
}
// Reads from the given file. Returns false in case of error.
bool LSTMRecognizer::DeSerialize(const TessdataManager* mgr, TFile* fp) {
delete network_;
network_ = Network::CreateFromFile(fp);
if (network_ == NULL) return false;
bool include_charsets = mgr == nullptr ||
!mgr->IsComponentAvailable(TESSDATA_LSTM_RECODER) ||
!mgr->IsComponentAvailable(TESSDATA_LSTM_UNICHARSET);
if (include_charsets && !ccutil_.unicharset.load_from_file(fp, false))
return false;
if (!network_str_.DeSerialize(fp)) return false;
if (fp->FReadEndian(&training_flags_, sizeof(training_flags_), 1) != 1)
return false;
if (fp->FReadEndian(&training_iteration_, sizeof(training_iteration_), 1) !=
1)
return false;
if (fp->FReadEndian(&sample_iteration_, sizeof(sample_iteration_), 1) != 1)
return false;
if (fp->FReadEndian(&null_char_, sizeof(null_char_), 1) != 1) return false;
if (fp->FReadEndian(&adam_beta_, sizeof(adam_beta_), 1) != 1) return false;
if (fp->FReadEndian(&learning_rate_, sizeof(learning_rate_), 1) != 1)
return false;
if (fp->FReadEndian(&momentum_, sizeof(momentum_), 1) != 1) return false;
if (include_charsets && !LoadRecoder(fp)) return false;
if (!include_charsets && !LoadCharsets(mgr)) return false;
network_->SetRandomizer(&randomizer_);
network_->CacheXScaleFactor(network_->XScaleFactor());
return true;
}
// Loads the charsets from mgr.
bool LSTMRecognizer::LoadCharsets(const TessdataManager* mgr) {
TFile fp;
if (!mgr->GetComponent(TESSDATA_LSTM_UNICHARSET, &fp)) return false;
if (!ccutil_.unicharset.load_from_file(&fp, false)) return false;
if (!mgr->GetComponent(TESSDATA_LSTM_RECODER, &fp)) return false;
if (!LoadRecoder(&fp)) return false;
return true;
}
// Loads the Recoder.
bool LSTMRecognizer::LoadRecoder(TFile* fp) {
if (IsRecoding()) {
if (!recoder_.DeSerialize(fp)) return false;
RecodedCharID code;
recoder_.EncodeUnichar(UNICHAR_SPACE, &code);
if (code(0) != UNICHAR_SPACE) {
tprintf("Space was garbled in recoding!!\n");
return false;
}
} else {
recoder_.SetupPassThrough(GetUnicharset());
training_flags_ |= TF_COMPRESS_UNICHARSET;
}
return true;
}
// Loads the dictionary if possible from the traineddata file.
// Prints a warning message, and returns false but otherwise fails silently
// and continues to work without it if loading fails.
// Note that dictionary load is independent from DeSerialize, but dependent
// on the unicharset matching. This enables training to deserialize a model
// from checkpoint or restore without having to go back and reload the
// dictionary.
bool LSTMRecognizer::LoadDictionary(const char* lang, TessdataManager* mgr) {
delete dict_;
dict_ = new Dict(&ccutil_);
dict_->SetupForLoad(Dict::GlobalDawgCache());
dict_->LoadLSTM(lang, mgr);
if (dict_->FinishLoad()) return true; // Success.
tprintf("Failed to load any lstm-specific dictionaries for lang %s!!\n",
lang);
delete dict_;
dict_ = NULL;
return false;
}
// Recognizes the line image, contained within image_data, returning the
// ratings matrix and matching box_word for each WERD_RES in the output.
void LSTMRecognizer::RecognizeLine(const ImageData& image_data, bool invert,
bool debug, double worst_dict_cert,
const TBOX& line_box,
PointerVector<WERD_RES>* words) {
NetworkIO outputs;
float scale_factor;
NetworkIO inputs;
if (!RecognizeLine(image_data, invert, debug, false, &scale_factor, &inputs,
&outputs))
return;
if (search_ == NULL) {
search_ =
new RecodeBeamSearch(recoder_, null_char_, SimpleTextOutput(), dict_);
}
search_->Decode(outputs, kDictRatio, kCertOffset, worst_dict_cert, NULL);
search_->ExtractBestPathAsWords(line_box, scale_factor, debug,
&GetUnicharset(), words);
}
// Helper computes min and mean best results in the output.
void LSTMRecognizer::OutputStats(const NetworkIO& outputs, float* min_output,
float* mean_output, float* sd) {
const int kOutputScale = MAX_INT8;
STATS stats(0, kOutputScale + 1);
for (int t = 0; t < outputs.Width(); ++t) {
int best_label = outputs.BestLabel(t, NULL);
if (best_label != null_char_) {
float best_output = outputs.f(t)[best_label];
stats.add(static_cast<int>(kOutputScale * best_output), 1);
}
}
// If the output is all nulls it could be that the photometric interpretation
// is wrong, so make it look bad, so the other way can win, even if not great.
if (stats.get_total() == 0) {
*min_output = 0.0f;
*mean_output = 0.0f;
*sd = 1.0f;
} else {
*min_output = static_cast<float>(stats.min_bucket()) / kOutputScale;
*mean_output = stats.mean() / kOutputScale;
*sd = stats.sd() / kOutputScale;
}
}
// Recognizes the image_data, returning the labels,
// scores, and corresponding pairs of start, end x-coords in coords.
bool LSTMRecognizer::RecognizeLine(const ImageData& image_data, bool invert,
bool debug, bool re_invert,
float* scale_factor, NetworkIO* inputs,
NetworkIO* outputs) {
// Maximum width of image to train on.
const int kMaxImageWidth = 2560;
// This ensures consistent recognition results.
SetRandomSeed();
int min_width = network_->XScaleFactor();
Pix* pix = Input::PrepareLSTMInputs(image_data, network_, min_width,
&randomizer_, scale_factor);
if (pix == NULL) {
tprintf("Line cannot be recognized!!\n");
return false;
}
if (network_->IsTraining() && pixGetWidth(pix) > kMaxImageWidth) {
tprintf("Image too large to learn!! Size = %dx%d\n", pixGetWidth(pix),
pixGetHeight(pix));
pixDestroy(&pix);
return false;
}
// Reduction factor from image to coords.
*scale_factor = min_width / *scale_factor;
inputs->set_int_mode(IsIntMode());
SetRandomSeed();
Input::PreparePixInput(network_->InputShape(), pix, &randomizer_, inputs);
network_->Forward(debug, *inputs, NULL, &scratch_space_, outputs);
// Check for auto inversion.
float pos_min, pos_mean, pos_sd;
OutputStats(*outputs, &pos_min, &pos_mean, &pos_sd);
if (invert && pos_min < 0.5) {
// Run again inverted and see if it is any better.
NetworkIO inv_inputs, inv_outputs;
inv_inputs.set_int_mode(IsIntMode());
SetRandomSeed();
pixInvert(pix, pix);
Input::PreparePixInput(network_->InputShape(), pix, &randomizer_,
&inv_inputs);
network_->Forward(debug, inv_inputs, NULL, &scratch_space_, &inv_outputs);
float inv_min, inv_mean, inv_sd;
OutputStats(inv_outputs, &inv_min, &inv_mean, &inv_sd);
if (inv_min > pos_min && inv_mean > pos_mean && inv_sd < pos_sd) {
// Inverted did better. Use inverted data.
if (debug) {
tprintf("Inverting image: old min=%g, mean=%g, sd=%g, inv %g,%g,%g\n",
pos_min, pos_mean, pos_sd, inv_min, inv_mean, inv_sd);
}
*outputs = inv_outputs;
*inputs = inv_inputs;
} else if (re_invert) {
// Inverting was not an improvement, so undo and run again, so the
// outputs match the best forward result.
SetRandomSeed();
network_->Forward(debug, *inputs, NULL, &scratch_space_, outputs);
}
}
pixDestroy(&pix);
if (debug) {
GenericVector<int> labels, coords;
LabelsFromOutputs(*outputs, &labels, &coords);
DisplayForward(*inputs, labels, coords, "LSTMForward", &debug_win_);
DebugActivationPath(*outputs, labels, coords);
}
return true;
}
// Converts an array of labels to utf-8, whether or not the labels are
// augmented with character boundaries.
STRING LSTMRecognizer::DecodeLabels(const GenericVector<int>& labels) {
STRING result;
int end = 1;
for (int start = 0; start < labels.size(); start = end) {
if (labels[start] == null_char_) {
end = start + 1;
} else {
result += DecodeLabel(labels, start, &end, NULL);
}
}
return result;
}
// Displays the forward results in a window with the characters and
// boundaries as determined by the labels and label_coords.
void LSTMRecognizer::DisplayForward(const NetworkIO& inputs,
const GenericVector<int>& labels,
const GenericVector<int>& label_coords,
const char* window_name,
ScrollView** window) {
#ifndef GRAPHICS_DISABLED // do nothing if there's no graphics
Pix* input_pix = inputs.ToPix();
Network::ClearWindow(false, window_name, pixGetWidth(input_pix),
pixGetHeight(input_pix), window);
int line_height = Network::DisplayImage(input_pix, *window);
DisplayLSTMOutput(labels, label_coords, line_height, *window);
#endif // GRAPHICS_DISABLED
}
// Displays the labels and cuts at the corresponding xcoords.
// Size of labels should match xcoords.
void LSTMRecognizer::DisplayLSTMOutput(const GenericVector<int>& labels,
const GenericVector<int>& xcoords,
int height, ScrollView* window) {
#ifndef GRAPHICS_DISABLED // do nothing if there's no graphics
int x_scale = network_->XScaleFactor();
window->TextAttributes("Arial", height / 4, false, false, false);
int end = 1;
for (int start = 0; start < labels.size(); start = end) {
int xpos = xcoords[start] * x_scale;
if (labels[start] == null_char_) {
end = start + 1;
window->Pen(ScrollView::RED);
} else {
window->Pen(ScrollView::GREEN);
const char* str = DecodeLabel(labels, start, &end, NULL);
if (*str == '\\') str = "\\\\";
xpos = xcoords[(start + end) / 2] * x_scale;
window->Text(xpos, height, str);
}
window->Line(xpos, 0, xpos, height * 3 / 2);
}
window->Update();
#endif // GRAPHICS_DISABLED
}
// Prints debug output detailing the activation path that is implied by the
// label_coords.
void LSTMRecognizer::DebugActivationPath(const NetworkIO& outputs,
const GenericVector<int>& labels,
const GenericVector<int>& xcoords) {
if (xcoords[0] > 0)
DebugActivationRange(outputs, "<null>", null_char_, 0, xcoords[0]);
int end = 1;
for (int start = 0; start < labels.size(); start = end) {
if (labels[start] == null_char_) {
end = start + 1;
DebugActivationRange(outputs, "<null>", null_char_, xcoords[start],
xcoords[end]);
continue;
} else {
int decoded;
const char* label = DecodeLabel(labels, start, &end, &decoded);
DebugActivationRange(outputs, label, labels[start], xcoords[start],
xcoords[start + 1]);
for (int i = start + 1; i < end; ++i) {
DebugActivationRange(outputs, DecodeSingleLabel(labels[i]), labels[i],
xcoords[i], xcoords[i + 1]);
}
}
}
}
// Prints debug output detailing activations and 2nd choice over a range
// of positions.
void LSTMRecognizer::DebugActivationRange(const NetworkIO& outputs,
const char* label, int best_choice,
int x_start, int x_end) {
tprintf("%s=%d On [%d, %d), scores=", label, best_choice, x_start, x_end);
double max_score = 0.0;
double mean_score = 0.0;
int width = x_end - x_start;
for (int x = x_start; x < x_end; ++x) {
const float* line = outputs.f(x);
double score = line[best_choice] * 100.0;
if (score > max_score) max_score = score;
mean_score += score / width;
int best_c = 0;
double best_score = 0.0;
for (int c = 0; c < outputs.NumFeatures(); ++c) {
if (c != best_choice && line[c] > best_score) {
best_c = c;
best_score = line[c];
}
}
tprintf(" %.3g(%s=%d=%.3g)", score, DecodeSingleLabel(best_c), best_c,
best_score * 100.0);
}
tprintf(", Mean=%g, max=%g\n", mean_score, max_score);
}
// Helper returns true if the null_char is the winner at t, and it beats the
// null_threshold, or the next choice is space, in which case we will use the
// null anyway.
static bool NullIsBest(const NetworkIO& output, float null_thr,
int null_char, int t) {
if (output.f(t)[null_char] >= null_thr) return true;
if (output.BestLabel(t, null_char, null_char, NULL) != UNICHAR_SPACE)
return false;
return output.f(t)[null_char] > output.f(t)[UNICHAR_SPACE];
}
// Converts the network output to a sequence of labels. Outputs labels, scores
// and start xcoords of each char, and each null_char_, with an additional
// final xcoord for the end of the output.
// The conversion method is determined by internal state.
void LSTMRecognizer::LabelsFromOutputs(const NetworkIO& outputs,
GenericVector<int>* labels,
GenericVector<int>* xcoords) {
if (SimpleTextOutput()) {
LabelsViaSimpleText(outputs, labels, xcoords);
} else {
LabelsViaReEncode(outputs, labels, xcoords);
}
}
// As LabelsViaCTC except that this function constructs the best path that
// contains only legal sequences of subcodes for CJK.
void LSTMRecognizer::LabelsViaReEncode(const NetworkIO& output,
GenericVector<int>* labels,
GenericVector<int>* xcoords) {
if (search_ == NULL) {
search_ =
new RecodeBeamSearch(recoder_, null_char_, SimpleTextOutput(), dict_);
}
search_->Decode(output, 1.0, 0.0, RecodeBeamSearch::kMinCertainty, NULL);
search_->ExtractBestPathAsLabels(labels, xcoords);
}
// Converts the network output to a sequence of labels, with scores, using
// the simple character model (each position is a char, and the null_char_ is
// mainly intended for tail padding.)
void LSTMRecognizer::LabelsViaSimpleText(const NetworkIO& output,
GenericVector<int>* labels,
GenericVector<int>* xcoords) {
labels->truncate(0);
xcoords->truncate(0);
int width = output.Width();
for (int t = 0; t < width; ++t) {
float score = 0.0f;
int label = output.BestLabel(t, &score);
if (label != null_char_) {
labels->push_back(label);
xcoords->push_back(t);
}
}
xcoords->push_back(width);
}
// Returns a string corresponding to the label starting at start. Sets *end
// to the next start and if non-null, *decoded to the unichar id.
const char* LSTMRecognizer::DecodeLabel(const GenericVector<int>& labels,
int start, int* end, int* decoded) {
*end = start + 1;
if (IsRecoding()) {
// Decode labels via recoder_.
RecodedCharID code;
if (labels[start] == null_char_) {
if (decoded != NULL) {
code.Set(0, null_char_);
*decoded = recoder_.DecodeUnichar(code);
}
return "<null>";
}
int index = start;
while (index < labels.size() &&
code.length() < RecodedCharID::kMaxCodeLen) {
code.Set(code.length(), labels[index++]);
while (index < labels.size() && labels[index] == null_char_) ++index;
int uni_id = recoder_.DecodeUnichar(code);
// If the next label isn't a valid first code, then we need to continue
// extending even if we have a valid uni_id from this prefix.
if (uni_id != INVALID_UNICHAR_ID &&
(index == labels.size() ||
code.length() == RecodedCharID::kMaxCodeLen ||
recoder_.IsValidFirstCode(labels[index]))) {
*end = index;
if (decoded != NULL) *decoded = uni_id;
if (uni_id == UNICHAR_SPACE) return " ";
return GetUnicharset().get_normed_unichar(uni_id);
}
}
return "<Undecodable>";
} else {
if (decoded != NULL) *decoded = labels[start];
if (labels[start] == null_char_) return "<null>";
if (labels[start] == UNICHAR_SPACE) return " ";
return GetUnicharset().get_normed_unichar(labels[start]);
}
}
// Returns a string corresponding to a given single label id, falling back to
// a default of ".." for part of a multi-label unichar-id.
const char* LSTMRecognizer::DecodeSingleLabel(int label) {
if (label == null_char_) return "<null>";
if (IsRecoding()) {
// Decode label via recoder_.
RecodedCharID code;
code.Set(0, label);
label = recoder_.DecodeUnichar(code);
if (label == INVALID_UNICHAR_ID) return ".."; // Part of a bigger code.
}
if (label == UNICHAR_SPACE) return " ";
return GetUnicharset().get_normed_unichar(label);
}
} // namespace tesseract.
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